<html><head><title>Machine Learning Engineer - Los Angeles, CA 90067</title></head>
<body><h2>Machine Learning Engineer - Los Angeles, CA 90067</h2>
<div><div><div><div><b>Company Overview</b></div><div></div><br/>
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Bain &amp; Company is the management consulting firm that the world’s business leaders come to when they want results. Bain advises clients on strategy, operations, information technology, organization, private equity, digital transformation and strategy, and mergers and acquisition, developing practical insights that clients act on and transferring skills that make change stick. The firm aligns its incentives with clients by linking its fees to their results. Bain clients have outperformed the stock market 4 to 1. Founded in 1973, Bain has 57 offices in 36 countries, and its deep expertise and client roster cross every industry and economic sector.</div><div></div><br/>
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Department Overview</b></div><div></div><br/>
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Bain’s Advanced Analytics Group is a team of high-impact quantitative technology specialists who solve statistical, machine learning, and data engineering challenges that we encounter in client engagements. AAG team members hold advanced degrees in subjects ranging across statistics, mathematics, computer sciences and other quantitative disciplines, and have backgrounds in a variety of fields including data science, marketing analytics and academia.</div><div></div><br/>
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Position Summary</b></div><div></div><br/>
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You will work on cutting-edge problems for a variety of different industries as a software engineer specializing in Machine Learning. As a member of a diverse engineering team, you will participate in the full engineering life cycle which includes designing, developing, optimizing, testing and deploying machine learning solutions to support experimentation and innovation, at the production scale of the world’s largest companies. This position is not a research position.</div><div></div><br/>
</div></div></div><div><div><div><div><b>Core Responsibilities</b></div><ul><li>Partner with Data Science, Data Engineering, and Infrastructure teams to develop and deploy production quality code</li><li>Develop and champion machine learning concepts technical audience and business stakeholders</li><li>Implement new and innovative machine learning tools, algorithms, and techniques within Bain and our clients</li><li>This position will be located in Palo Alto, Los Angeles, Boston, Dallas, Austin, or Seattle</li><li><b>Travel is required (~20%)</b></li></ul><div>
Build and deploy machine learning solutions to solve business problems</div><ul><li>Build end to end machine learning solutions that solve business problems using analytics and deploy them into a scalable cloud or client’s on-premise infrastructure</li><li>Utilize state-of-the art ML and statistical algorithms for common business problems such as time series forecasting, anomaly detection, personalization, natural language processing</li><li>Model deployment: identify operations bottlenecks for ML systems and optimize choices of technologies (e.g. distributed systems), model compression (serialization, pruning and quantization), and serving latency tuning while minimizing impact on model performance</li><li>Deliver business insights using both custom built and off-the-shelf data engineering and data science tools</li></ul><div>
Develop production data science infrastructure and deployment tooling</div><ul><li>Participate in the full software development life cycle including reviewing distributed system designs, writing documentation and unit/integration tests, and conducting code reviews</li><li>Improve internal and client systems infrastructure including CI/CD, microservice frameworks, and cloud infrastructure needed to support ML and data engineering workloads</li></ul><div>
Provide technical guidance to external clients and internal stakeholders in Bain</div><ul><li>Explore new technology innovations to determine better ways to provide improved customer results</li><li>Advise and coach engineering teams on technology stack best practices to raise their machine learning development and deployment capabilities</li><li>Mentor and coach data science team on the best software development practices and help them speed up their work</li></ul>
</div></div></div><div><div><div><div><b>Qualifications</b></div><div></div><br/>
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Required:</b></div><ul><li>Bachelor’s, Master’s Degree or PhD in a quantitative discipline such as Computer Science, Engineering, Physics, Econometrics, Statistics, or Information Sciences such as business analytics or informatics</li><li>4+ years of experience with data science, machine learning</li><li>2+ years of experience building and supporting highly scalable, reliable, available, resilient, distributed and parallel production grade computing systems or machine learning solutions</li><li>Preparing data for analytics, building predictive models, and driving innovation in the modeling process</li><li>2+ years of experience working on public cloud environments (AWS, GCP, or Azure)</li><li>Expert in Python and SQL</li><li>Proficient in one or more of R, Java, C++, Scala, and Go</li><li>Strong computer science fundaments in data structures, algorithms, automated testing, object-oriented programming, performance complexity, and implications of computer architecture on software performance</li><li>Machine learning frameworks and tools (e.g. Pandas, numpy, scikit-learn, mlr, caret, H2O, TensorFlow, MXNet, Pytorch, Caffe/Caffe2, CNTK, and MLlib)</li><li>Data ingestion using one or more modern ETL compute and orchestration frameworks (e.g. Apache Airflow, Luigi, Spark, Apache Nifi, and Apache Beam)</li><li>Version control and git workflows</li></ul><div><b>
Preferred:</b></div><ul><li>Strong foundational understanding of statistics concepts and algorithms including linear/logistic regression, random forest, boosting, NNs, etc</li><li>Relational and NoSQL databases</li><li>Deployment best practices using CI/CD tools and infrastructure as code (Jenkins, Docker, Kubernetes, and Terraform)</li><li>Strong interpersonal and communication skills, including the ability to explain and discuss mathematical and machine learning technicalities with colleagues and clients from other disciplines</li><li>Agile development methodology</li></ul><div><b>Bonus:</b></div><div></div><br/>
<ul><li>Open source distributed computing and database frameworks such as Apache Flink, Ignite, Presto, Apex, Cassandra, and HBase</li><li>Data warehousing and analytical database technologies such as Vertica, CitusDB, MapD, and Kinetica</li><li>Engineering distributed systems and database internals (including handling consensus, availability, and distributed query processing)</li><li>Deep learning frameworks such as Chainer, Theano, and Deeplearning4J</li><li>GPU programming experience with CUDA</li><li>JVM numerical libraries such as ND4S, Breeze, JBlas, and MLlib</li><li>Elements of the PyData ecosystem including Cython, Numba, Dask, Spacy, and Gensim</li></ul><div></div><br/>
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IND123</div></div></div></div></body>
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